The objective of this study is to develop and use a novel high-resolution vegetation optical depth dataset based on ESA’s Sentinel-1 satellites to improve our understanding on the local impacts of water availability on vegetation at a global scale using novel machine learning approaches. We will use Copernicus Sentinel-1 in combination with MetOp ASCAT VOD to 1) establish quantitative relationships between Sentinel-1 backscatter and ratios thereof and MetOp ASCAT VOD, to then 2) develop a high-resolution 1 km VOD product sensitive to changes in water content of the above ground biomass. The newly developed VOD will be 3) evaluated using different ESA and non-ESA EO datasets, among which are CGLS LAI and ESA’s Earth Explorer SMOS VOD. Finally the high-resolution VOD will be used to 4) quantify the effect of water availability on vegetation dynamics for different land cover types at the local scale.